Different solutions have been described or are commercially available to allow for acquire environments for purposes such as localization or mapping. Different approaches have given rise to different solutions.
Among these, a number of commercial and prototype indoor navigation systems are based on inertial sensors (e.g. DLR's FootSLAM, Chirange Geospatial Indoor Tracking). They are small and inexpensive, however the position accuracy is low and drifts significantly over time. Furthermore, inertial systems do not generate map information. Therefore, they are only suitable for positioning and navigation purposes, not for map generation.
Other indoor positioning systems are based on the transmission of radio signals—similarly to GPS signals in outdoor environments. Some system use existing infrastructure (e.g. WiFi networks in airports, Navizon), others require the installation of dedicated infrastructure (e.g. NextNav, SenionLab). The systems have virtually no sensor costs (the client application uses a smart phone with dedicated software application), but they require network infrastructure emitting the radio signal. Furthermore, they do not generate map information. Therefore, they are only suitable for positioning and navigation purposes, not for map generation.
A further interesting product uses 3D scanning. ZEB1 is a commercial product that uses 3D laser scanning for fast (indoor) mapping. The laser is mounted on a spring and an oscillating movement needs to be created by hand. It generates an accurate 3D model of the indoor environment. However, the system does not provide immediate feedback to the user, as data processing is carried out off-line. Hence, the system is suitable for mapping but not for real-time localization.
A still further solution is a laser backpack developed at UC Berkley. It is a R&D project which proposes a backpack equipped with several 2D line scanners used to generate a 3D model of indoor environments. Again, it does not provide for on-line visualization.
A last solution is called LOAM (Lidar Odometry and Mapping) and consists of a portable sensor with associated algorithms that combine laser scanning and video imagery for real-time localization and mapping.
Almost all these solutions lack real-time/on-line visualization and more importantly they do not allow for any direct user interaction on the acquiring and processing steps.
US2014/005933A1 discloses a system and method for mapping parameter data acquired by a robot mapping system. Parameter data characterizing the environment is collected while the robot localizes itself within the environment using landmarks. Parameter data is recorded in a plurality of local grids, i.e., sub-maps associated with the robot position and orientation when the data was collected. The robot is configured to generate new grids or reuse existing grids depending on the robot's current pose, the pose associated with other grids, and the uncertainty of these relative pose estimates. The pose estimates associated with the grids are updated over time as the robot refines its estimates of the locations of landmarks from which it determines its pose in the environment. Occupancy maps or other global parameter maps may be generated by rendering local grids into a comprehensive map indicating the parameter data in a global reference frame extending the dimensions of the environment.
TIMOTHY LIU ET AL: “Indoor localization and visualization using a human-operated backpack system”, INDOOR POSITIONING AND INDOOR NAVIGATION (IPIN), 2010 INTERNATIONAL CONFERENCE ON, IEEE, PISCATAWAY, N.J., USA, 15 Sep. 2010 (2010-09-15), pages 1-10, XP031809367, ISBN: 978-1-4244-5862-2 discloses techniques for indoor localization and visualization using a human-operated backpack system equipped with 2D laser scanners and inertial measurement units (IMU), in which scan matching based algorithms are used to localize the backpack in complex indoor environments. To address misalignment between successive images used for texturing when building 3D textured models, the authors propose an image based pose estimation algorithm to refine the results from the scan matching based localization.
WO2015/017941A1 discloses systems and methods for generating data indicative of a three-dimensional representation of a scene. Current depth data indicative of a scene is generated using a sensor. Salient features are detected within a depth frame associated with the depth data, and these salient features are matched with a saliency likelihoods distribution. The saliency likelihoods distribution represents the scene, and is generated from previously-detected salient features. The pose of the sensor is estimated based upon the matching of detected salient features, and this estimated pose is refined based upon a volumetric representation of the scene. The volumetric representation of the scene is updated based upon the current depth data and estimated pose. A saliency likelihoods distribution representation is updated based on the salient features. Image data indicative of the scene may also be generated and used along with depth data.